You are asked to demonstrate your recently acquired skills in risk management in a business context. The project will also prepare you gradually for the final Risk Management hedging project.

Framing the Problem

  • You were recently hired by a top tier Investment Bank, EverRight Securities.
  • EverRight hires MBAs both for their expertise and professionalism in communication with clients.
  • EverRight was recently approached by GreatPlains to produce a fact-based short paper to understand context and risk associated with Oil and Natural Gas in Canada.
  • GreatPlains is a consortium of senior executives with a background in extracting industries and require this knowledge to better understand the economics of monetizing resources after a molecule is produced.

Skills Practiced & Requirements

  • Use of APIs for Enterprise workflow i.e. strictly no Excel, csv or use of other local files.

  • Data wrangling and parsing. You must use tidy data with long and wide data frames.

  • Choice of effective visualization in communication.

  • Data analytics and an ability to tell a story from it.

  • Foundational risk management skills.

  • Clean, clear and concise rendering of a business document. You may refer to the Rmarkdown cheatsheet or any other online document to customize your output. I reserve the right to award a maximum of 50% for any document not properly presented. Some characteristics of a properly presented document:

    • A clear heading with title and who it is from.
    • Cleaning organized by business questions asked i.e. not a technical step rendering of your workflow.
    • Charts properly formatted and self explanatory.
    • …

  • Each question should be answered as if you were presenting to a client: you would not show code to a client!

  • Learning to learn: Using new packages and functions as you see fit to achieve the desired outcome.

Whilst there are many packages to format tables the following are suggested:

Data

You will use the following sources of data:

  1. You will be using data from the US Energy Information Administration and the API webpage is available at EIA Open Data. You may refer to the bootcamp section 7.3 after you have registered and obtained your own API key. The eia, EIAdata and RTL packages provide functions to access the API once you have your API key.

  2. Morningstar Commodities for futures contracts using RTL::getPrices() and your credentials.

  3. The RTL::fizdiffs randomized data sets of Crude oil prices in key trading locations across North America:

    • Edmonton, AB.
    • Hardisty, AB.
    • Cushing, OK.
    • Houston (Nederland), TX.

Simplified NA Energy Infrastructure Maps

Your Ask

Using Rmarkdown, render into a clean, clear, concise html document to be sent to the client.

Name your file firstNameLastName.Rmd and send it to pcote@ualberta.ca.

Part A: Canadian Exports (3.5 points)

GreatPlains needs context on the level and trends of Canadian exports to the US since 2010-01-01.

Use the following data from the EIA to tell the story.

Part B: Commodity Valuation (5 points)

The benchmark crude oil grade in Alberta is Western Canadian Select ("WCS"). GreatPlains executives need factual information on the value of WCS a producer may obtain by selling in Alberta or exporting to Houston. This export additional value is then compared to the costs and risks of taking on pipeline commitments.

Using the RTL::fizdiffs randomized dataset below, prepare the following in your report:

  1. Plot a time series of the values in Alberta, Houston and the price difference that could be realized by selling the crude in Houston instead of Alberta (a delivery location arbitrage).
  2. Show what would be the average gain of exporting from Alberta to Houston.
  3. Compute and discuss the average daily risk in dollars per barrel for the value in Alberta, in Houston and in the export arbitrage.
RTL::fizdiffs %>% 
  dplyr::transmute(date = date,
                   Alberta = WTI.CMA01 + WCS.HDY,
                   Houston = WTI.CMA01 + WCS.HOU)
## # A tibble: 1,440 × 3
##    date       Alberta Houston
##    <date>       <dbl>   <dbl>
##  1 2017-01-03    38.2    45.6
##  2 2017-01-04    39.0    46.4
##  3 2017-01-05    39.4    46.8
##  4 2017-01-06    40.8    47.4
##  5 2017-01-09    39.0    45.2
##  6 2017-01-10    38.3    44.2
##  7 2017-01-11    39.4    45.3
##  8 2017-01-12    40.5    46.3
##  9 2017-01-13    39.9    45.6
## 10 2017-01-17    40.8    45.8
## # … with 1,430 more rows

Part C: Delivery Timing (12.5 points)

GreatPlains clients have at times to make decisions on temporary storage and all they see in the financial news headlines is the price of WTI crude.

They are considering making decisions on storing from the 2nd to the 3rd contract month.

You, as a specialist, know that there are futures contracts for different delivery months and using data from Morningstar from 2010-01-01 to 2023-01-31:

  1. Describe and explain the relevance of the futures contract delivery location, settlement date and settlement method.
  2. Chart the time series of this storage spread over time assuming zero costs of storage and financing.
  3. With 2020-05-15 futures prices, compute whether it would economical to store assuming a financing cost of 5% per annum and monthly storage costs of $0.40 per barrel.
  4. As different firms may have different working capital costs, show a sensitivity chart of the value of storage versus working capital costs between 1% and 10%. Use mapply() as covered in class or purrr::pmap() if you want to experiment. We will use the latter for more complex risk management problems at a later stage.

Part D: Project Financing (4 points)

GreatPlains often has to provide advice on matters of financing terms. In this specific case, the client has the choice of financing for:

  • A 7-year term that will then be refinanced at maturity for the remaining 3 years.
  • A full 10-year term.

The problem they have is that their usual decision-making process is of low quality: they have no idea as of today what the implied refinancing rate is and therefore engage in broad qualitative conversations around the future path of interest rates.

  1. Assuming interest rates are simple annual compounding, what is the implied 3-year annualized refinancing rate?
  2. Assuming interest rates are continuous compounding, what is the implied 3-year annualized refinancing rate?
bankOffer
## # A tibble: 5 × 2
##   maturity   rate
##      <dbl>  <dbl>
## 1        1 0.03  
## 2        3 0.035 
## 3        5 0.04  
## 4        7 0.0425
## 5       10 0.05

Grading

This is strictly an individual exercise. Any attempt to communicate or cooperate with any other person will be deemed a violation of Academic Integrity and Honesty.

See Grading section of the class notes for more details.